Cost-effectiveness of screening with transcriptional signatures for incipient TB among U.S. migrants
Bibliographic record
Abstract
Introduction: Host-response-based transcriptional signatures (HrTS) have been developed to identify "incipient tuberculosis (TB)". No study has reported the cost-effectiveness of HrTS for post-arrival migrant screening programs in low-incidence countries. Objectives: To assess the potential health impact and cost-effectiveness of HrTS for post-arrival TB infection screening among new migrants in the United States. Methods: We used a discrete-event simulation model to compare four strategies: (1) no screening for TB infection or incipient TB; (2) 'IGRA-only', screen all with interferon gamma release assay (IGRA), provide TB preventive treatment for IGRA-positives; (3) 'IGRA-HrTS', screen all with IGRA followed by HrTS for IGRA-positives, provide incipient TB treatment for individuals testing positive with both tests; and (4) 'HrTS-only', screen all with HrTS, provide incipient TB treatment for HrTS-positives. We assessed outcomes over the lifetime of migrants entering the U.S. in 2019, assuming HrTS met the WHO Target Product Profile (TPP) optimal criteria. We conducted sensitivity analyses to evaluate the robustness of results. Results: The IGRA-only strategy dominated the HrTS-based strategies under both healthcare sector and societal perspectives, with an incremental cost-effectiveness ratio of $78,943 and $89,431 per quality-adjusted life-years (QALY) gained, respectively. This conclusion was robust to varying costs ($15-300) and characteristics of HrTS, and the willingness-to-pay threshold ($30,000-150,000/ QALY gained), but sensitive to the rate of decline in TB progression risk after U.S. entry. Conclusions: Our findings suggest that HrTS meeting the WHO TPP is unlikely to be a cost-effective component of post-arrival screening for migrants entering the U.S.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".